Abstract
There is a great deal of variability in estimates of the lifetime medical care cost externality of obesity, partly due to a lack of transparency in the methodology behind these cost models. Several important factors must be considered in producing the best possible estimate, including age-related weight gain, differential life expectancy, identifiability, and cost model selection. In particular, age-related weight gain represents an important new component to recent cost estimates. Without accounting for age-related weight gain, a study relies on the untenable assumption that people remain the same weight throughout their lives, leading to a fundamental misunderstanding of the evolution and development of the obesity crisis. This study seeks to inform future researchers on the best methods and data available both to estimate age-related weight gain and to accurately and consistently estimate obesity's lifetime external medical care costs. This should help both to create a more standardized approach to cost estimation as well as encourage more transparency between all parties interested in the question of obesity's lifetime cost and, ultimately, evaluating the benefits and costs of interventions targeting obesity at various points in the life course.
Highlights
As the cost and prevalence of obesity continue to soar, it has become more important than ever to produce accurate estimates of the lifetime medical care cost externality.Downloaded from https://www.cambridge.org/core
While Markov models have been widely used in the estimation of body mass index (BMI) growth curves, we found most articles explain relatively little of their methodology or why the Markov model provides an ideal fit for the problem
There are a variety of potential datasets suited to the task, including the Framingham Heart Study (FHS), Coronary Artery Disease Risk in Adults (CARDIA), the Health and Retirement Study (HRS), the Medicare Current Beneficiary Survey (MCBS), and the Atherosclerosis Risk in Communities (ARIC)
Summary
As the cost and prevalence of obesity continue to soar, it has become more important than ever to produce accurate estimates of the lifetime medical care cost externality. A policy-relevant estimate should (i) cover obesity’s costs over the life course, (ii) focus on third-party costs (the externality imposed on others), and (iii) account for changes in BMI over time Each of these points is developed . While Markov models have been widely used in the estimation of BMI growth curves, we found most articles explain relatively little of their methodology or why the Markov model provides an ideal fit for the problem This manuscript seeks to demystify the recent literature on the lifetime social costs of obesity by detailing the advantages and pitfalls of applying a Markov model to measure age-related weight gain, possibilities for causal inference in future models, discussing methodological considerations in adopting the appropriate cost model, and demonstrating how to account for differences in life expectancy between people with obesity and normal weight people. We conclude by discussing data requirements, how age-related weight gain affects cost estimates, and limits to existing estimates and data availability
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